Data analytics and the cloud – a compelling mix

Cloud computing – combined with connectivity and bandwidth – forms the backbone of digitization. Only this technology can ultimately bring the expected applications of the Internet of Things (IoT) to life. Within just a few years, billions of machines, sensors and other components will be networked with one another and these networked devices will collect and transmit billions of items of data every day. To evaluate data profitably, companies need storage capacity and data analytics should ideally be possible in real time. This is where the cloud is needed.

Hybrid cloud meets requirements of digitization

Companies and organizations will find it worthwhile to investigate the issue in further detail. The hybrid cloud in particular, i.e. the mix of public and private cloud, becomes increasingly important: “The key role being played by the hybrid cloud in implementing the digital transformation is confirmed by the decision-makers interviewed. They see the greatest need for hybrid clouds in typical digitization areas. In addition to business process automation (28 percent), these include big data analytics and customer self-services to improve the customer experience (both 27 percent) and development of new business models (24 percent),” report the analysts from IDC (International Data Corporation).

There are clearly many reasons for hybrid scenarios in the big data context. While the IT and business decision-makers interviewed state that only sensitive personnel data, certain financial and accounting data, and research and development data remain on their own in-house servers, an increasing volume of data is finding a home in the public cloud. Furthermore, say the experts from market researcher IDC, the immense capacity of the public cloud is vital to meet the new needs of the digital age. Hybrid cloud environments, for instance, can be used to flexibly expand the storage and computing capacity of a company’s own data center for testing or marketing campaigns with IaaS. Another example is at the application level, where the now standard CRM from the cloud can be linked to a permanently installed ERP solution.

Key success factor: analytical tools

Software manufacturer Tableau offers a further important argument in favor of the successful cloud and big data combination: it contends that the vast volumes of structured and unstructured data produced by the Internet of Things will increasingly be provided by cloud services. This data would often be heterogeneous and would be located on numerous relational and non-relational systems (such as Hadoop clusters and NoSQL databases). This leads to the following result: “While innovations in storage and managed services have accelerated the data capture process, accessing and understanding the data itself remain the biggest challenges in the final phase. This has led to a rising demand for analytical tools that can create a seamless link to a large number of data sources hosted in the cloud and combine these,” say the experts from Tableau. Companies could use these types of tools to examine and visualize all types of data, wherever it is stored. This is essential for identifying hidden business opportunities in IoT investment.

Ultimately, it is also a question of cost, which makes everything apart from cloud-based big data strategies now seem obsolete. Alongside other cost-specific aspects, a Deloitte study highlights faster payback times (30 percent) and greater agility (29 percent) as key reasons for moving to the cloud or introducing hybrid data management concepts. “56 percent of the managers interviewed from IT and specialist departments are planning a hybrid cloud strategy within the next 12 to 24 months – less than a fifth of respondents are using an on-site approach as the primary rollout model,” say the authors of the study, referring to companies’ current big data strategies.

In conclusion, the cloud is essential as an interface for the various classes of data and applications. Furthermore, only a hybrid cloud can deliver the necessary flexibility for digitization – an absolute must in the context of the Internet of Things.